This Truffle language exposes JSONPath query execution to the polyglot GraalVM.

The goals are:

1. Present a couple of parsing techniques based on structural indexes to quickly execute queries on JSON files
2. Introduce a batching approach to improve performance and allow the processing of datasets bigger than the GPU’s memory
3. Implement the above concepts into a Truffle Language to provide an engine that can be used from any host language that can run on the GraalVM.

1. GpJSON requires GrCUDA to use the GPU. Install the custom GrCUDA version from necst: https://github.com/necst/grcuda.
2. GrCUDA require GraalVM 21.3. Download from https://github.com/graalvm/graalvm-ce-builds/releases.
3. Checkout to commit GRCUDA-133-gpjson-2.0 in GrCUDA and install it.
4. Now switch to GraalVM 22.1. You can use SDKman to change GraalVM versions quickly.
5. Compile GpJSON with GraalVM 22.1. Use the mvn package command.
6. Copy both GrCUDA and GpJSON jars to the GraalVM22.1 languages folder.
7. Use GraalVM 22.1 for experiments with GpJSON.

To compile a JAR file containing GpJSON move to the language folder and run mvn package.

Next, copy the JAR file from target/gpjson.jar into jre/languages/gpjson (Java 8) or languages/gpjson (Java 11) of the Graal installation.

Note that --jvm and --polyglot must be specified in both cases as well.

In the examples folder, you can find a couple of files containing examples of the GpJSON's syntax.

To run the benchmarks provided in the benchmarks folder you first need to install the following dependencies:

• CUDA Toolkit
• GraalVM Community Edition
• gcc
• Node.js

Then, add the following variables to your .bashrc (or equivalent):

export CUDA_DIR=[your-cuda-path]
export PATH=$PATH:$CUDA_DIR/bin
export GRAAL_DIR=[your-graalvm-path]
export PATH=$PATH:$GRAAL_DIR/bin
export NODE_DIR=[your-node-path]

Copy the grcuda and gpjson JARs from the deliverables folder to [your-graalvm-path]/languages/[grcuda/gpjson]/.

Move to the benchmarks folder cd benchmarks and run make setup to install jsonpath, jsonpath-plus and simdjson.

Finally, run ./[name-of-the-benchmark].sh. Results will be saved to [name-of-the-benchmark].csv.

The following options can be added to the command above:

• -g to exclude the GPU-based benchmarks (GpJSON only). Default is false
• -w [number] to set the number of warmup runs. Default is 5
• -r [number] to set the number of runs. Default is 10
• -t [number] to set the number of threads (Java JSONPath only). Default is 11
• -d [path] to set the path of the dataset. Default value is /home/ubuntu/datasets-ext/

Datasets can be downloaded here.

The plotting notebooks use Python and Jupyter Lab. Create a conda environment and install the dependencies.

conda create -n rapids-24.10 -c rapidsai -c conda-forge -c nvidia  \
    cudf=24.10 python=3.11 'cuda-version>=12.0,<=12.5'
pip install -r requirements.txt

The paper plots.ipynb notebook generates the plots in the paper. The rapids_vs_gpjson/rapids plots.ipynb notebook generates the plots for the RAPIDS vs GpJSON section of the paper.

For further details, such as the versions of the dependencies used or the queries executed by the benchmarks suite, please refer to the official thesis and/or publication.